10 results about "Sentence segmentation" patented technology

The application provides a role-aware chapter theme event argument extraction method and device, and the method comprises the following steps: obtaining argument role information of a chapter theme event of an event type according to the event type; performing sentence segmentation and title extraction on a target article to obtain a sentence set and an event title; the argument role information, the event type, and the event title constitute event-related information; constructing an argument role-aware graph by using the event-related information and the sentence set, performing event-related sentence detection, and obtaining a chapter theme event-related sentence set; taking the chapter theme event-related sentence set as input, constructing a question for each argument role, predicting all candidate arguments in the chapter theme event-related sentence set, and screening out a target argument from the candidate arguments. The method improves the model effect while maintaining the flexibility of the model.

The application discloses a multi-intention sentence segmentation method and device, a storage medium and an electronic device, relates to the technical field of smart homes, and comprises the following steps: extracting the character features of the characters included in a to-be-segmented sentence carrying multiple execution intentions; converting the to-be-segmented sentence into a feature image according to the character features, wherein the feature image is used to represent the character features through image pixels; performing semantic segmentation on the feature image to generate multiple semantic images, wherein each semantic image is used to represent a target execution intention in the multiple execution intentions; and identifying the target sub-sentence corresponding to each semantic image in the multiple semantic images to obtain multiple target sub-sentences corresponding to the to-be-segmented sentence. By adopting the technical scheme, the problems, such as low segmentation efficiency, in the process of segmenting multi-intention sentences in the related art are solved.

The application relates to the technical field of natural language processing, in particular to an AI generated text detection method and system based on a deep learning model. The method comprises the following steps: performing character normalization and sentence segmentation on a to-be-detected text, segmenting the text by a sliding window to generate a segmented sequence; constructing deep semantic discrimination features, logarithmic probability difference trace features, style structure features and entity consistency features; inputting a gate fusion discrimination network, outputting a generated probability and confidence degree after probability calibration, and outputting a segmented contribution degree; performing threshold adaptive determination according to a risk level, a text length and the confidence degree, outputting a review state and generating a traceable record when the confidence degree is insufficient; and triggering incremental updating based on drift monitoring and a feedback sample pool. The application improves cross-domain robustness and interpretability, reduces false positives and false negatives, and supports long-term stable operation.

The application relates to the technical field of natural language processing, and discloses a Chinese numeral quantifier correction method and system, computer equipment and a storage medium. The application constructs a dynamically expandable collocation word table and a numeral dictionary; performs sentence segmentation, word segmentation and part-of-speech tagging on input text, and identifies a three-element structure composed of numerals, quantifiers and nouns; excludes misjudgments on inherent expressions through a fixed phrase filtering mechanism; performs legality verification on the structure based on the collocation word table, and generates processing information for semantic correction if the verification fails; when collocation abnormalities are confirmed and the dictionary is insufficient, a large language model is triggered to replace the quantifier and adapt to the context based on the processing information, so that a final corrected sentence is generated. Through multi-layer cooperation of rule matching, word table verification and large model verification, the application effectively reduces the mis-correction rate while ensuring high-precision correction, and improves the accuracy and practicality of automatic correction of Chinese quantifiers.

The application provides a machine-generated text detection method, system, device and medium, and belongs to the field of natural language processing. The method comprises the following steps: performing sentence segmentation on an original text; generating a perturbed text through a four-layer structure perception perturbator, wherein the perturbator dynamically selects a perturbation type and intensity through a learnable strategy; reorganizing the text through a pre-trained encoding-decoding model and extracting a feature vector; fusing the multi-source semantic and structural features of the original text, the perturbed text and the reorganized text; and inputting a classifier to output a text detection result in combination with a comprehensive loss function. The problem of low detection accuracy of the text after machine polishing is solved.

The application provides an intelligent question and answer method and system fusing a rock knowledge graph, relates to the cross technical field of artificial intelligence and geological engineering, and aims at the problems of fragmented knowledge in the rock field, non-uniform terms, weak reasoning ability, weak professional property of intelligent question and answer, poor practicability and low accuracy. The method comprises the following steps: acquiring multi-source text data, and pre-processing the multi-source text data; performing sentence segmentation and standardization processing on the pre-processed multi-source text data; performing concurrent multi-relation triple extraction through a subject trigger type multi-relation joint extraction mechanism; constructing an ontology structure level, fusing the multi-relation triple, obtaining a rock knowledge graph, analyzing a user question and performing graph query, adopting an intention perception mechanism, performing graph reasoning on the graph retrieval content through an R-GCN model, and generating a natural language answer from the graph reasoning result. The application solves the problems of fragmented knowledge in the rock field and weak reasoning ability, and realizes accurate and rapid question answering in the rock field.

The application provides a remote supervision relationship extraction method based on multi-granularity contrast learning, and relates to the technical field of information extraction. The method comprises the following steps: obtaining a sentence in a data sample; segmenting the sentence into a word sequence; marking the head and tail of the word sequence respectively to obtain a marked sentence; obtaining initial sentence features by using a bidirectional encoder; obtaining initial positive sample features by using a positive example pair encoder; obtaining constraint features of a relationship and an entity pair according to a graph encoder and fusing the constraint features into corresponding initial sentence features and initial positive sample features to obtain enhanced sentence features and enhanced positive sample features; fusing the enhanced sentence features into bag-level features; inputting the bag-level features and the enhanced positive sample features into a contrast learning relationship classifier to extract the relationship of the data sample and obtain a classified relationship. The application solves the problem of low accuracy and efficiency of relationship extraction and weak long-tail relationship extraction capability caused by ignoring the interaction between different hierarchical features.

The present application discloses a conversation translation method and a related apparatus. The method comprises: when an earphone detects that an earphone-side user speaks, collecting a first speech; the earphone sending the first speech to a terminal device; and when the earphone detects the earphone-side user segmenting a sentence, sending a first identifier to the terminal device, and on the basis of the first identifier, the terminal device translating the first speech to obtain a translation result of the first speech. In this way, a user wearing the earphone for conversation translation can complete speech reception, sentence segmentation, and translation of the user without manual operations, thereby effectively reducing user operations during conversation translation of the user, improving the translation accuracy, and improving user experience.

This application provides a method, apparatus, electronic device, and storage medium for constructing a multi-domain knowledge graph, relating to the field of knowledge graph construction technology. The method includes: constructing a text dataset and performing domain filtering based on a pre-selected large language model to obtain domain text datasets for target domains (single-domain, related-domain, and full-domain); further performing sentence segmentation, word segmentation, and part-of-speech tagging; extracting nouns from the segmented word dataset to obtain target entities; performing word segmentation and dependency syntax relation extraction on each sentence in the target domain text dataset, and reconstructing a dictionary list of target dependency syntax relations to extract relationships between different domains; and constructing a multi-domain knowledge graph based on entities and relations. The multi-domain knowledge graph constructed in this application is more comprehensive, accurate, and efficient, requiring no large amounts of data for model training, thus saving significant time, manpower, and computational resources.